ShinyPitch

Saurav
17th April 2018

1. Goal

We are modelling the airquality dataset in R.

  • We will try to model the Solar Radiation Data
  • We will use the Ozone levels
  • And then we will use the Temperature levels to model the data

2. Summary of the airquality data

     Ozone           Solar.R           Wind             Temp      
 Min.   :  1.00   Min.   :  7.0   Min.   : 1.700   Min.   :56.00  
 1st Qu.: 18.00   1st Qu.:115.8   1st Qu.: 7.400   1st Qu.:72.00  
 Median : 31.50   Median :205.0   Median : 9.700   Median :79.00  
 Mean   : 42.13   Mean   :185.9   Mean   : 9.958   Mean   :77.88  
 3rd Qu.: 63.25   3rd Qu.:258.8   3rd Qu.:11.500   3rd Qu.:85.00  
 Max.   :168.00   Max.   :334.0   Max.   :20.700   Max.   :97.00  
 NA's   :37       NA's   :7                                       
     Month            Day      
 Min.   :5.000   Min.   : 1.0  
 1st Qu.:6.000   1st Qu.: 8.0  
 Median :7.000   Median :16.0  
 Mean   :6.993   Mean   :15.8  
 3rd Qu.:8.000   3rd Qu.:23.0  
 Max.   :9.000   Max.   :31.0

3. Plots of the data

plot of chunk unnamed-chunk-2

4. Model using ozone data

m1 <- lm(Solar.R ~ Ozone, data = airquality)
g <- qplot(data = airquality, x = Ozone, y = Solar.R) + geom_abline(slope = m1$coefficients[2], intercept = m1$coefficients[1])
g

plot of chunk unnamed-chunk-3

5. Model using Temperature data

m1 <- lm(Solar.R ~ Temp, data = airquality)
g2 <- qplot(data = airquality, x = Temp, y = Solar.R) + geom_abline(slope = m1$coefficients[2], intercept = m1$coefficients[1])
g2

plot of chunk unnamed-chunk-4